CODING FOR RISK MANAGEMENT
Equips students with the ability to adopt the programming culture typically present in the ERM/risk areas of most financial organizations. By studying Python, SQL, R, git, and AWS, students gain exposure to different syntaxes. Students apply these skills by coding up market risk and credit risk models. Students also gain familiarity with working in the cloud.
- Department: Enterprise Risk Management
- M 8:10 p.m.-10 p.m.
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Instructor:
David J Romoff
Info - 3 points
FINANCIAL RISK MANAGEMENT
A survey of market, credit, liquidity, and systemic risk. Includes case studies, risk quantification methods, and common mitigation techniques using portfolio management, hedging, and derivatives. Also addresses traditional risk management practices at banking institutions.
- Department: Enterprise Risk Management
- R 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
David J Romoff
Info -
Instructor:
Zaur Nurtaza
Info -
Instructor:
Navin Sharma
Info -
Instructor:
Justin Hon
Info - 3 points
MATH MTHDS-FIN PRICE ANALYSIS
Course covers modern statistical and physical methods of analysis and prediction of financial price data. Methods from statistics, physics and econometrics will be presented with the goal to create and analyze different quantitative investment models.
- Department: Mathematics
- S 7 p.m.-9:20 p.m.
- 3 points
SPORT ACCOUNTING & FINANCE
- Department: Sports Management
- T 6:10 p.m.-8 p.m.
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Instructor:
Destinee Coles
Info - 3 points
Agroecology - A Natural Climate Solution
Agroecology A Natural Cli
Natural climate solutions (NCS) refer to actions aimed at protecting, better managing, and restoring nature to achieve climate goals. Adopting sustainable, climate-smart agricultural practices following agroecology principles provides a cost-effective NCS pathway to mitigate climate change, while also ensuring food security and environmental sustainability. This course will introduce the principles of agroecology, the key concepts of carbon and nitrogen dynamics, as well as the commonly adopted agroecological practices across various agricultural landscapes, including croplands, grasslands, and agroforestry systems. A combination of lectures, discussions, and field activities will be utilized to demonstrate how agroecological practices can be monitored in terms of their influence on ecosystem services.
This course will prepare students to apply principles of sustainability science to improved soil and agricultural management, addressing the growing need for better adoption of land based NCS. This course will also delve into the technological aspects of NCS monitoring that will help working professionals in conservation, environmental, and sustainable business organizations develop the necessary skills to evaluate the outcomes of sustainable land management practices to inform management decisions, policy making, and incentive-based programs. This elective course aims to connect scientific methods with decision-making processes to prepare students to be leaders in sustainability and make impacts on both local and large-scale climate issues.
QUANTITATIVE RISK MANAGEMENT
Quantitative Risk Management continues building your quantitative foundation in order to work with more advanced models and use mathematical and statistical intuition for building those models. At the end of this course, you will be able to use analytics algorithms for risk management; use factor models to assess the quality of investment portfolios and trader positions; hedge equity, option, and fixed-income portfolios using derivatives; estimate volatility with options models and GARCH models; and model ESG and Climate risk.
The course is highly structured and organized by topic into semester long learning threads. Each week, readings and assignments will take another step forward along these threads: regression models, classification models, time series analysis, options and volatility modeling, fixed income modeling, factor models and portfolio management, tail risk modeling. These concepts will be demonstrated in python and students are expected to be able to understand and run python code.
- Department: Enterprise Risk Management
- W 8:10 p.m.-10 p.m.
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Instructor:
David J Romoff
Info - 3 points
INSURANCE RISK MANAGEMENT
Review of types of insurance risk, such as pricing risk, underwriting risk, reserving risk, etc. Includes case studies, risk quantification methods (e.g., market-consistent economic capital models, dynamic financial analysis (DFA) models, catastrophe models, etc.), and common mitigation techniques, such as asset-liability management (ALM), reinsurance, etc. Also addresses traditional risk management at insurance companies and ERM actuarial standards of practice (ASOPs).
MULTI-ASSET PORTFOLIO MGMT
The course will cover practical issues such as: how to select an investment universe and instruments, derive long term risk/return forecasts, create tactical models, construct and implement an efficient portfolio,to take into account constraints and transaction costs, measure and manage portfolio risk, and analyze the performance of the total portfolio.
- Department: Mathematics
- MW 6:10 p.m.-7:25 p.m.
- 3 points
CREDIT RISK MANAGEMENT
Credit Risk Management requires business acumen, the monitoring of internal and external data, disciplined execution, and organizational intelligence. A solid understanding of this enables a credit risk manager to help organizations achieve their objectives. Through readings, case studies, and modeling projects, students learn how risk managers decide on credit risk management strategy applied throughout the client lifecycle.
- Department: Enterprise Risk Management
- T 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Christakis Droussiotis
Info -
Instructor:
Weijun Weng
Info - 3 points
CAPSTONE
Capstone projects afford a group of students the opportunity to undertake complex, real-world, client-based projects for nonprofit organizations, supervised by a Nonprofit Management program faculty member. Through the semester-long capstone project, students will experience the process of organizational assimilation and integration as they tackle a discrete management project of long or short-term benefit to the client organization. The larger theoretical issues that affect nonprofit managers and their relationships with other stakeholders, both internal and external, will also be discussed within the context of this project-based course.
- Department: Non Profit Management
- T 6:30 p.m.-8 p.m.
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Instructor:
Basil A Jr Smikle
Info - 3 points
SPORTS REVENUES STRATEGIES & ANALYSIS
Digital, social, and mobile media continue to heavily impact every aspect of sports business, often in profound and unanticipated ways, particularly in managing and optimizing revenue streams. All revenue line items are fully intertwined and integrated with each other, media, sponsorship, ticketing, hospitality, concessions and licensing, etc. Students of this course will learn to analyze and optimize the ecosystem of sports business including content rights, ticketing, sponsorship, merchandising, marketing, etc., as well as make business analytics decisions by leveraging business analytics software to run scenario analysis.
- Department: Sports Management
- R 6:10 p.m.-8 p.m.
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Instructor:
Ishwara Glassman Chrein
Info - 3 points
MA Mentored Research
MA MENTORED RESEARCH
This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
- Department: Statistics
-
Instructor:
Demissie Alemayehu
h-index: 14 Info - 0-3 points
Statistical Fieldwork
Prerequisites: GR5203; GR5204 &GR5205 and at least 4 approved electives This course is an elective course for students in the M.A. in Statistics program that counts towards the degree requirements. To receive a grade and academic credits for this course, students are expected to engage in approved off-campus internships that can be counted as an elective. Statistical Fieldwork should provide students an opportunity to apply their statistical skills and gain practical knowledge on how statistics can be applied to solve real-world challenges.
- Department: Statistics
-
Instructor:
Demissie Alemayehu
h-index: 14 Info - 1 points
FUNDAMENTALS OF DATA ENGINEERING
MANAGING DATA
FUNDAMENTALS OF DATA ENGINEERING
- Department: Applied Analytics
- R 6:10 p.m.-8 p.m.
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Instructor:
Amreeta Choudhury
Info - 3 points
FUNDAMENTALS OF DATA ENGINEERING
MANAGING DATA ONLINE
FUNDAMENTALS OF DATA ENGINEERING
- Department: Applied Analytics
- W 6:10 p.m.-8 p.m.
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Instructor:
Robert Simione
Info - 3 points
CLINICAL ETHICS
While this course is designed to introduce students to the fundamentals of clinical ethics and the basic terminology and framework of ethical analysis in biomedical ethics, it offers a more sociological perspective, putting the contemporary clinical issues into a broader context. We will look briefly at the development of clinical ethics and its impact on hospital care and doctor-patient relationships, on the prevailing autonomy norm and its critique. The course then focuses on issues encountered in clinical practice such as informed consent, patient capacity, decision-making, end of life, advance directives, medical futility, pediatrics ethics, maternal-fetal conflicts, organ transplantation, cultural competence and diversity of beliefs and others. The course will examine the role of the clinical ethics consultant (CEC) and assignments will mimic the work that CECs may perform in the hospital setting.
Over the span of the semester, students become familiar with the ethical questions surrounding major topics in the clinic with a practical case-based approach toward ethics dilemmas and ethics consultation. During the semester, students in New York attend a meeting of the adult or pediatric ethics committees of New York Presbyterian and Morgan Stanley Children's Hospital or another area hospital, as well as ethics lectures given at the medical center.
Students are expected to complete five case write-ups using a template that will be given by the instructor. Students will be using these cases to refine and hone their ethical analysis skills and to show their knowledge of law, policy and ethical principles and how they might apply to each situation.
- Department: Bioethics
- W 2 p.m.-4 p.m.
- R 9:30 a.m.-11:30 a.m.
- W 8 p.m.-10 p.m.
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Instructor:
Barbra Rothschild
Info - 3 points
OPERATIONS: BACKBONE OF THE COMPANY
OPERATIONS: BACKBONE OF C
- Department: Insurance Management
-
Instructor:
Amanda Bill
Info - 3 points
TAX PLANNING
The Tax Planning course explores the various methods of the U.S. tax system, its development, its applicability to individual (and corporate) taxpayers, and steps taxpayers of various income and wealth levels take to determine,
meet, and minimize their tax obligations, depending on their goals. Students will learn how to identify sources, nature, and taxability of taxpayers’ income and gains, to determine the deductibility of any expenses they incur to reduce income, identify credits they may have to offset taxes due, understand filing and payment obligations, and apply the methods of minimizing tax - avoidance, deferral, and use of lower brackets or realization by other taxpayers.
- Department: Wealth Management
-
Instructor:
Adam Ludman
Info - 3 points
MARKET RISK MANAGEMENT
This course provides the tools to measure and manage market risk in the context of large financial institutions. The volume and complexity of the data itself, at large institutions, makes it a challenge to generate actionable information. We will take on this challenge to master the path from data to decisions.
We cover the essential inputs to the engines of financial risk management: VaR, Expected Exposure, Potential Exposure, Expected Shortfall, backtesting, and stress testing as they apply to asset management and trading. We explore the strengths and weaknesses of these different metrics and the tradeoffs between them. We also cover how regulatory frameworks impact both the details and the strategy of building these engines. Lastly, we cover counterparty-credit methodologies, mainly as they apply to Trading Book risk.
- Department: Enterprise Risk Management
- W 6:10 p.m.-8 p.m.
- T 6:10 p.m.-8 p.m.
-
Instructor:
Michael Zwecher
Info -
Instructor:
Deniz Ozenbas
Info - 3 points
LEADERSHIP IN SPORTS MANAGEMENT
LDRSHP & PERS MGMT IN SPR
TBA
- Department: Sports Management
- T 4:10 p.m.-6 p.m.
- 3 points
LEADERSHIP IN SPORTS MANAGEMENT
LDRSHP & PERS MGMT IN SPR
TBA
- Department: Sports Management
- T 4:10 p.m.-6 p.m.
- 3 points
LEADERSHIP IN SPORTS MANAGEMENT
TBA
- Department: Sports Management
- T 4:10 p.m.-6 p.m.
-
Instructor:
Mary Scott
Info -
Instructor:
Leonard Elmore
Info - 3 points
Printmaking on, through, and below the m
Print on, through, & belo
This course offers to the student who may find an examination of printmaking an asset to their art practice.
The course will cover several printmaking processes like relief, intaglio, silkscreen, and monotype. In addition, we will discuss printmaking concepts such as repetition, matrix, original/translation, reproducibility, and multiple considering the works produced in class.
We will involve a separate in-depth study of each process by alternating studio time, demonstrations, field trips, individual and group critiques.
Through the printmaking processes, students will explore assignments and projects and be encouraged to incorporate them into their own body of work.
- Department: Visual Arts
- F 10 a.m.-4 p.m.
-
Instructor:
Craig Zammiello
Info - 3 points
SYMBOLIC LOGIC
Advanced introduction to classical sentential and predicate logic. No previous acquaintance with logic is required; nonetheless a willingness to master technicalities and to work at a certain level of abstraction is desirable. Note: Due to significant overlap, students may receive credit for only one of the following three courses: PHIL UN3411, UN3415, GR5415.
- Department: Philosophy
- MW 2:40 p.m.-3:55 p.m.
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Instructor:
Achille Varzi
CULPA: 30
Wikipedia
h-index: 40
Info
- 4 points
CREDIT RISK ANALYTICS
The field of credit risk management is undergoing a quiet revolution as subjective and manually-intensive methods give way to digitization, algorithmic management, and decision-making. This course provides a practical overview and hands-on experience with different methods, and it also provides a view of future technologies and discussions of potential future directions. Participants in this course should be well-positioned to take entry-level analytic positions and help drive strategic decisions.
The first half of the course explores analytics used today for credit risk management. You will learn to create rating and scoring models and a macro scenario-based stress testing model. In the second half of the course, we explore more advanced tools used by the more prominent organizations and fintech firms, including neural net and XGBoost decision tree models.
- Department: Enterprise Risk Management
- M 8:10 p.m.-10 p.m.
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Instructor:
Christopher Mann
Info - 3 points
QUALITATIVE EVALUATION METHODS FOR NARRA
QUALTV EVALUATION METHODS
TBA
- Department: Narrative Medicine
- 3 points
PRINTMAKING I: INTAGLIO
- Department: Visual Arts
- MW 1:30 p.m.-4 p.m.
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Instructor:
Jennifer Nuss
Info - 3 points
PRINTMAKING II: INTAGLIO
- Department: Visual Arts
- MW 1:30 p.m.-4 p.m.
-
Instructor:
Jennifer Nuss
Info - 3 points
LIQUIDITY RISK MANAGEMENT
Indicators of companies running into hard times typically include revenue volatility, loss of key personnel, reputational damage, and increased litigation. However, company failures are frequently marked by insufficient liquidity, or the lack of cash to meet obligations. Liquidity risk is the unexpected change in a company’s cash resources or demands on such resources that results in the untimely sale of assets, and/or an inability to meet contractual demands and/or default. In extreme cases, the lack of sufficient cash creates severe losses and results in company bankruptcy.
An institution’s cash resources and obligations can and must be managed. Indeed, the field of liquidity risk management is an established part of treasury departments at sizable institutions. The regularity of cash flows and the turbulence of business and markets must be assessed and quantified. This course provides students the tools and techniques to manage all types of liquidity challenges including the need to sell assets unexpectedly in the market, or work through ‘‘run‐on-the‐bank’’ situations for financial services companies.
- Department: Enterprise Risk Management
- T 8:10 p.m.-10 p.m.
-
Instructor:
Joseph Dziwura
Info - 3 points
Machine Learning for Finance
The application of Machine Learning (ML) algorithms in the Financial industry is now commonplace, but still nascent in its potential. This course provides an overview of ML applications for finance use cases including trading, investment management, and consumer banking.
Students will learn how to work with financial data and how to apply ML algorithms using the data. In addition to providing an overview of the most commonly used ML models, we will detail the regression, KNN, NLP, and time series deep learning ML models using desktop and cloud technologies.
The course is taught in Python using Numpy, Pandas, scikit-learn and other libraries. Basic programming knowledge in any language is required.
- Department: Mathematics
- S 2:40 p.m.-4:30 p.m.
- 3 points
CREATIVITY IN BUSINESS
- Department: Business
- MTWRF 9 a.m.-5 p.m.
-
Instructor:
Faculty
h-index: 37 Info - 3 points
GLOBAL ETHICS
Increasingly, issues of medical research and clinical care are posing complex ethical issues not only in the United States, but in other countries in both the industrialized and the developing world. Yet varying economic, political, social, cultural, and historical contexts shape these issues. In diverse contexts in Asia, Africa, Europe and North and South America, practices and policies, along with cultures and moral values, differ enormously. Yet ethical issues are arising not in isolation, but as part of global communities and discourses. In research, multinational pharmaceutical companies are increasingly conducting studies in both industrialized countries and the developing world, posing numerous ethical tensions. In clinical care, uses of reproductive technologies differ across national borders, leading to “reproductive tourism”. End of life care varies widely, reflecting in part differing attitudes toward death and dying. This course examines the political, economic, social, cultural, philosophical, medical, and historical roots and implications of these issues.
The course meets once a week online for an hour and a half, and offers extensive live-session interaction and post-session discussion forums to explore the various bioethical issues contemplated throughout the semester.
- Department: Bioethics
- R 4:10 p.m.-6 p.m.
- T 6 p.m.-7:45 p.m.
-
Instructor:
Robert L Klitzman
Wikipedia Info - 3 points
BLOCKCHAIN AND ERM
Using Blockchain, decisions can be made without relying on a single centralized authority, allowing for greater transparency and trust between participants. By using smart contracts and distributed ledgers, users can easily create, modify, and manage agreements between stakeholders, ensuring that all parties have access to the same information and can make informed decisions. As a result, Blockchain technology reduces the risks associated with decision-making, and improves efficiency and accuracy. This course first examines the risks and rewards of implementing Blockchain at large organizations engaging in decentralized decision-making processes. The course then explores the Blockchain as a tool for risk management.
- Department: Enterprise Risk Management
- W 8:10 p.m.-10 p.m.
-
Instructor:
Ivan Bakrac
Info - 3 points
PRINTMAKING I: SILKSCREEN
- Department: Visual Arts
- R 10 a.m.-4 p.m.
-
Instructor:
Liz Schneider
Info - 3 points
IMPACT FINANCE FOR SUSTAINABILITY PRACTI
IMPACT FINANCE FOR SUST PRACT
This survey course examines a range of sustainable and impact investing fixed income and equity products
before transitioning to the asset owner perspective on sustainable and impact investing. Each class session
includes elements of financial analysis, financial structure, social or environmental impact, and policy and
regulatory context. Brief guest lectures, podcasts, and three experiential exercises bring these topics to life.
At the end of the course, each student will be able to (i) construct a diversified portfolio of impact
investments based on the range of products tackled in class, (ii) integrate ESG into debt and equity valuation,
(iii) develop an impact investing product that an asset manager or investment bank could launch, (iv) develop
an impact investing strategy for an asset owner, and (v) lead either side of the investor-corporate dialogue on
sustainability. The lectures are designed to prepare students for both the impact investing product
development exercise and the impact investing asset owner strategy exercise, and these two exercises are
designed to prepare students for impact investing leadership over the course of their careers.
As an early innovator in social finance, dating back 24 years, the instructor provides students with a practical
toolkit, honed by making mainstream financial institutions and products more beneficial to a broader range
of stakeholders and making specialist impact investment firms more relevant to and integrated with
mainstream markets.
- Department: Sustainability Management
- R 6:10 p.m.-8 p.m.
-
Instructor:
Bhakti Mirchandani
Info - 3 points
Sustainability Value Creation in Private
This course provides an overview of the way sustainability (environmental, social and governance) factors are analyzed in private markets. It focuses on preparing students to implement their understanding of the financial and societal risks and opportunities within the investment making process. In private markets, limited partners (pension funds, endowments, high net-worth individuals) have pushed the sustainability imperative and social consciousness of private equity funds and asset managers by seeking greater clarity around how their money is invested in both a responsible and financially meaningful way. Alongside this trend, an evolving regulatory environment globally has propelled the need to systemize evaluation frameworks for stakeholders within investment functions and advisors who support them.Unlike public markets, sustainability information is harder to glean in private markets and requires a skilled extraction and evaluation process. During this course, we examine a traditional ESG due diligence process embedded within the wider investment lifecycle (sourcing, diligence, hold and exit) through the lens of changing geographic regulatory landscape in financial investing and the market leading frameworks that quantify ESG factors for evaluation. The course culminates with a deal due diligence process that mimics an investment committee (IC) comprised of private equity leaders that understand the commercial and purpose-driven viability of an investment.
- Department: Sustainability Management
- M 4:10 p.m.-6 p.m.
-
Instructor:
Angela Jhanji
Info - 3 points
CLOUD COMPUTING
Data analytics have become an essential component of business intelligence and informed decision making. Sophisticated statistical and algorithmic methodologies, generally known as data science, are now of predominant interest and focus. Yet, the underlying cloud computing platform is fundamental to the enablement of data management and analytics.
This course introduces students to cloud computing concepts and practices ranging from infrastructure and administration to services and applications. The course is primarily focused on the development of practical skills in utilizing cloud services to build distributed and scalable analytics applications. Students will have hands-on exposure to VMs (Virtual Machines), databases, storage, microservices, and AI/ML (Artificial Intelligence and Machine Learning) services through Google Cloud Platform, et al. Cost and performance characteristics of alternative approaches will also be studied. Topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems. Throughout, students will study state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, and IBM.
The course modules provide a blend of lecture and reading materials along with class exercises and programming assignments. While extensive programming experience is not required, students taking the course are expected to possess basic Python 3 programming skills.
The desired outcome of the course is the student’s ability to put conceptual knowledge to practical use. Whether you are taking this course for future academic research, for work in industry, or for an innovative startup idea, this course should help you master the fundamentals of cloud computing.
- Department: Applied Analytics
- T 6:10 p.m.-8 p.m.
- None None-None
- M 8:10 p.m.-10 p.m.
-
Instructor:
Mohsen Golalikhani
Info - 3 points
Credit Analytics
This course uses a combination of lectures and case studies to introduce students to the modern credit analytics. The objective for the course is to cover major analytic concepts, ideas with a focus on the underlying mathematics used in both credit risk management and credit valuation. We will start from an empirical analysis of default probabilities (or PD), recovery rates and rating transitions. Then we will introduce the essential concepts of survival analysis as a scientific way to study default. For credit portfolio we will study and compare different approaches such as CreditPortfolio View, CreditRisk+ as well as copula function approach. For valuation we will cover both single name and portfolio models.
- Department: Mathematics
- F 1:10 p.m.-3:40 p.m.
- 3 points
COGNITIVE LINGUISTICS
- Department: Latin American and Iberian Cultures
- TR 2:40 p.m.-3:55 p.m.
-
Instructor:
Reyes Llopis-Garcia
CULPA: 10
h-index: 10
Info
- 4 points
PRIVACY RISK MANAGEMENT
In today’s digital age, with the collection and usage of personal information growing at an exponential rate, the study of privacy risk management is crucial. As organizations grapple with the dual challenge of monetizing technological innovation without running afoul of regulatory and legal restrictions, the ERM professional who understands how to identify, assess, and manage privacy risk is in high demand. In this course, students will develop an understanding of the legal frameworks governing data usage, the ethical issues associated with the use of personal information, and how to develop robust privacy frameworks and controls in order to manage privacy risk.
- Department: Enterprise Risk Management
- T 6:10 p.m.-8 p.m.
-
Instructor:
Heather McArn
Info - 3 points
SPORTS LAW AND ETHICS
- Department: Sports Management
- W 6:10 p.m.-8 p.m.
- M 4:10 p.m.-6 p.m.
-
Instructor:
Carla Varriale
Info - 3 points
INTRO TO BLOCKCHAIN, CRYPTO$ & ANALYTICS
BLOCKCHAINCRYPTOCURRENCY &ANL
TBD
- Department: Applied Analytics
- T 6:10 p.m.-8 p.m.
- None None-None
-
Instructor:
Siddhartha Dalal
Info - 3 points
CLINICAL ETHICS: END OF LIFE CARE
This course will explore how medical decisions at the end of life regarding both curative and palliative care are influenced by medical, legal and philosophical principles and social norms. We will explore how the development of clinical practice standards for patients with advanced disease have evolved in response to advances in treatment and societal assertion of consumerist rights of self determination. This work will assist students in the Bioethics Masters Program to develop the consultation skills needed to interact effectively with patients, even under difficult circumstances. We will build on the knowledge base established in the Introduction to Clinical Ethics course (BIETPS5400) to develop the specific insights and skills needed by clinicians and other professionals to support patients and caregivers who confront dilemmas in end of life care management. Students will develop the analytic and communication techniques needed to advise treating clinicians who seek guidance on management of end of life care for patients, and communicating prognosis and treatment options to their patients’ families. The skills developed in this course will be useful to students who will confront ethical dilemmas in their roles as treating clinicians, health care administrators, ethics committee members, compliance officers, and patient advocates. This course will also provide a valuable foundation for students who intend to pursue more advanced training in clinical ethics consultation, in order to prepare for a career as an institutionally based Ethics Consultant.
- Department: Bioethics
- T 8 p.m.-10 p.m.
-
Instructor:
David N Hoffman
h-index: 2 Info - 3 points
AI and the Future of Strategic Communica
AI & the Future of Strat
The field of Artificial Intelligence (AI) has rapidly evolved to become a transformative global force across various industries, with particular significance for strategic communication. This elective course provides a comprehensive exploration of AI’s foundations, its current landscape, and its profound impact on media, journalism, public relations, and marketing communications. The course also addresses critical issues surrounding AI such as ethics, policy, and risk management associated with adoption, while offering practical insights into implementing common AI tools and developing essential AI skills for communication professionals.
- Department: Strategic Communication
- M 4:10 p.m.-6 p.m.
-
Instructor:
Irene Papajohn
Info - 3 points
A MATHEMATICAL APPROACH TO GENERATIVE AI
MATHMATCL APPRCH GENERATI
Generative AI (“GenAI") is reshaping the global economy and the future of work by revolutionizing problem-solving, optimizing complex systems, and enabling data-driven decision-making. Its profound impact spans across natural language understanding, image generation, and predictive analytics, marking a paradigm shift that necessitates a deep and rigorous understanding of its mathematical foundations. This course is designed to equip students with a comprehensive framework for exploring the mathematical principles underpinning GenAI. Emphasizing statistical modeling, optimization, and computational techniques, the curriculum provides the essential tools to develop and analyze cutting-edge generative models.
- Department: Mathematics
- S 4:45 p.m.-6:35 p.m.
- 3 points
CIRCULAR ECONOMY FOR SUSTAINABILITY PROF
Throughout history, societies have discovered resources, designed and developed them into textiles,
tools and structures, and bartered and exchanged these goods based on their respective values.
Economies emerged, driven by each society’s needs and limited by the resources and technology
available to them. Over the last two centuries, global development accelerated due in large part to the
overextraction and use of finite resources, whether for energy or materials, and supported by vast
technological advancements. However, this economic model did not account for the long-term impacts of
the disposal or depletion of these finite resources and instead, carried on unreservedly in a “take-make’-
waste” manner, otherwise known as a linear economy. Despite a more profound understanding of our
planet’s available resources, the environmental impact of disposal and depletion, and the technological
advancements of the last several decades, the economic heritage of the last two centuries persists today;
which begs the question: what alternatives are there to a linear economy?
The premise of this course is that through systems-thinking, interdisciplinary solutions for an alternative
economic future are available to us. By looking at resources’ potential, we can shape alternative methods
of procurement, design, application, and create new market demands that aim to keep materials,
products and components in rotation at their highest utility and value. This elective course will delve into
both the theory of a circular economy - which would be a state of complete systemic regeneration and
restoration as well as an optimized use of resources and zero waste - and the practical applications
required in order to achieve this economic model. Achieving perfect circularity represents potentially
transformative systemic change and requires a fundamental re-think of many of our current economic
structures, systems and processes.
This is a full-semester elective course which is designed to create awareness among sustainability
leaders that those structures, systems and processes which exist today are not those which will carry us
(as rapidly as we need) into a more sustaining future. The class will be comprised of a series of lectures,
supported by readings and case-studies on business models, design thinking and materi
- Department: Sustainability Management
- M 4:10 p.m.-6 p.m.
-
Instructor:
Diana A Trushell
Info - 3 points
MARKET RESEARCH
Market research is the way that companies identify, understand and develop the target market for their products. It is an important component of business strategy, and it draws on the research and analytics skills you have learned thus far in the program. Often market research consists of generating your own data, through quantitative and qualitative methodologies, in pursuit of the market research question.
This course is an elective that will expand on quantitative and qualitative methodologies that have been introduced previously, provide an introduction to other methodologies that are more specific to market research, and provide hands-on practice in defining a market research plan from start to finish. Students will also learn about particular types of market research studies and when and how they should be deployed. Students will generate and test their own research instruments. Through the use of case studies and simulations, students will learn how market research fits into an overarching marketing plan for a company.
This course is designed for students who have completed the Research Design and Strategy and Analytics core courses, and who are exploring how research fits into product marketing. You will leave this class understanding the essential aspects of market research, when and how they should be deployed, and the role you could play in small and large companies directing and executing on market research opportunities.
- Department: Applied Analytics
- T 8:10 p.m.-10 p.m.
-
Instructor:
Diahanna L Post
Info - 3 points
GENDER AND COMMUNICATION IN THE WORKPLAC
GENDER & COMM IN THE WORKPLACE
Gender and Communication in the Workplace offers professionals across sectors and industries the knowledge and skills needed to identify the social and linguistic practices enacted at work, and the opportunity to advance the interests of those who run up against barriers to advancement as a result of prejudice and stereotyping.
- Department: Strategic Communication
- M 2:10 p.m.-4 p.m.
- 3 points
AI USER INTERFACES FOR ANALYTICS
AI UI FOR ANALYTICS
In recent years, data analytics and artificial intelligence (AI) have become essential to business intelligence and informed decision making. But to realize the impact of analytics and AI, effective visual communication of data insights via user interfaces (UI), such as web pages and app dashboards, is equally critical. Building effective UIs requires mastering the user experience (UX) design principles and certain front-end development technologies. Furthermore, the recent rise of multimodal Generative AI offers unprecedented opportunities for simplifying, automating, and scaling UX/UI development.
This course provides a comprehensive understanding of UX design principles and best practices for developing UIs while emphasizing ethical considerations and inclusivity. Students will learn to create intuitive and visually engaging websites and dashboards that leverage AI-generated insights, also considering data privacy, diversity, and accessibility. Key topics include the design, implementation, and evaluation of UIs, with hands-on experience in web development technologies like HTML, CSS, and JavaScript, as well as related cloud services. Students will apply state-of-the-art AI technologies to create intelligent and interactive UIs, all while critically assessing data sources and AI models for potential biases.
The course content comprises a blend of conceptual learning and practice assignments. Weekly lectures and reading materials will cover the fundamentals of data visualization and user experience designs. Students will put the gained knowledge into practice through individual design and coding assignments and a group term project.
- Department: Applied Analytics
- W 8:10 p.m.-10 p.m.
- R 8:10 a.m.-10 a.m.
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Instructor:
Javid B Huseynov
Info - 3 points
PEDIATRICS & ETHICS
- Department: Bioethics
- T 4 p.m.-5:30 p.m.
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Instructor:
George Hardart
Info - 3 points
Supervised Research
Students conduct research related to biotechnology under the sponsorship of a mentor within the University. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
Supervised Research
Students conduct research related to biotechnology under the sponsorship of a mentor within the University. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
CAPSTONE PROJECT
CONSTRUCTION CAPSTONE PROJECT
- Department: Construction Administration
- R 6:10 p.m.-8 p.m.
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Instructor:
Evangelia Ieronymaki
Info - 3 points
GLOBAL LEGAL & REGULATORY ECOSYSTEM
- Department: Insurance Management
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Instructor:
Sandra Bograd
Info - 3 points
Intro to Moving Image: Video, Film & Art
Intro Moving Image:Video,
Intro to Moving Image: Video, Film & Art is an introductory class on the production and editing of digital video. Designed as an intensive hands-on production/post-production workshop, the apprehension of technical and aesthetic skills in shooting, sound and editing will be emphasized. Assignments are developed to allow students to deepen their familiarity with the language of the moving image medium. Over the course of the term, the class will explore the language and syntax of the moving image, including fiction, documentary and experimental approaches. Importance will be placed on the decision making behind the production of a work; why it was conceived of, shot, and edited in a certain way. Class time will be divided between technical workshops, viewing and discussing films and videos by independent producers/artists and discussing and critiquing students projects. Readings will be assigned on technical, aesthetic and theoretical issues. Only one section offered per semester. If the class is full, please visit http://arts.columbia.edu/undergraduate-visual-arts-program.
- Department: Visual Arts
- M 10 a.m.-4 p.m.
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Instructor:
Shelly Silver
CULPA: 1 Info - 3 points
Supervised Independent Research
Students conduct research related to biotechnology under the sponsorship of a mentor outside the University within the New York City Metropolitan Area unless otherwise approved by the Program. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
Supervised Independent Research
Students conduct research related to biotechnology under the sponsorship of a mentor outside the University within the New York City Metropolitan Area unless otherwise approved by the Program. The student and the mentor determine the nature and extent of this independent study. In some laboratories, the student may be assigned to work with a postdoctoral fellow, graduate student or a senior member of the laboratory, who is in turn supervised by the mentor. The mentor is responsible for mentoring and evaluating the students progress and performance. Credits received from this course may be used to fulfill the laboratory requirement for the degree. Instructor permission required. Web site: http://www.columbia.edu/cu/biology/courses/g4500-g4503/index.html
- Department: Biological Sciences
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Instructor:
Lili Yamasaki
CULPA: 5 Info - 3-6 points
COMPANY FAILURES
Examination of areas critical to an organization’s success from strategic, operational, financial, and insurance perspectives, and examines why many companies fail in spite of the vast knowledge of factors driving success. Several case studies examined in depth.
- Department: Enterprise Risk Management
- T 6:10 p.m.-8 p.m.
- W 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Adam H Litke
Info -
Instructor:
Tom Williams
Info -
Instructor:
Steve Lindo
Info - 3 points
MAFN FIELDWORK
Prerequisites: all 6 MAFN core courses, at least 6 credits of approved electives, and the instructors permission. See the MAFN website for details. This course provides an opportunity for MAFN students to engage in off-campus internships for academic credit that counts towards the degree. Graded by letter grade. Students need to secure an internship and get it approved by the instructor.
- Department: Mathematics
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Instructor:
Jaehyuk Choi
Info - 1-3 points
MATHEMATICS FOR AI
This course equips students with essential mathematical foundations for understanding and working with artificial intelligence (AI) algorithms. After a brief introduction to the historical and social context that numbers arise in, students will learn about:
- Linear Algebra: Matrices, matrix-vector multiplication, linear models, change of basis, dimensionality, spectral decomposition, and principal component analysis (PCA).
- Calculus: Rates of change, derivatives, optimization techniques like gradient descent, with a brief touch upon linear approximation.
- Probability and Statistics: Mathematically deriving complex probability distributions out of simpler ones, mathematically deriving statistical testing methods
- Graph Theory: How graphs are used to find relationships between data as well as being a setting for AI-driven problem solving.
- Problem Solving and Algorithms: Applying mathematical concepts to find problem solutions.
Students will learn about search methods like uninformed search, informed search with the A* algorithm, and greedy algorithms.
- Computational Theory and Automata: Answering questions about what is computable, what is needed in order to compute something, and using this framework to state how much “information” is contained in a mathematical object.
By the end of this course, students will possess a strong mathematical toolkit to confidently tackle the complexities of modern AI algorithms.
- Department: Applied Analytics
- W 6:10 p.m.-8 p.m.
- None None-None
- T 6:10 p.m.-8 p.m.
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Instructor:
Thilan Arachchi Appuhamillage
Info - 3 points
RISK REGULATIONS IN FINANCE
This course examines post-financial crisis regulations including Basel III, Fundamental Review of the Trading Book (FRTB), Dodd-Frank Act, Supervision and Regulation Letter 11-7 (SR 11-7), and others. Case studies will explore the technical details of these new rules; and guest lectures from industry experts will bring the material to life. Areas of focus include: model risk management, stress testing, derivatives, and insurance. By the end of this course students will be able to:
Evaluate the purpose and limitations of risk regulations in finance.
Identify and communicate weaknesses in a financial firm.
Communicate with regulators.
Understand Recovery and Resolution Plans or “Living Wills” for a financial firm.
- Department: Enterprise Risk Management
- T 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Jean Michel Ngalikpima
Info -
Instructor:
Eric Goetz
Info - 3 points
Career Development for Quantitative Fina
Career Develop for Quant
This course helps the students understand the job search process and develop the professional skills necessary for career advancement. The students will not only learn the best practices in all aspects of job-seeking but will also have a chance to practice their skills. Each class will be divided into two parts: a lecture and a workshop.
In addition, the students will get support from Teaching Assistants who will be available to guide and prepare the students for technical interviews.
- Department: Mathematics
- TR 11:40 a.m.-12:55 p.m.
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Instructor:
Jaehyuk Choi
Info - 0 points
ESG AND ERM
ESG will be a driving force in risk management in upcoming years. ERM / Risk professionals need a solid understanding of emerging ESG trends and regulations and how they apply to day-to-day job responsibilities. The ESG and ERM course begins with an overview of the ESG landscape and framework. After a foundational understanding is established, the course focuses on incorporating ESG into enterprise risk management, including identification, quantification, decision making, and reporting of ESG-related risks.
- Department: Enterprise Risk Management
- M 6:10 p.m.-8 p.m.
- T 8:10 p.m.-10 p.m.
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Instructor:
Helda Rock
Info -
Instructor:
Tahmina Day
Info - 3 points
CONSUMERISM AND SUSTAINABILITY
TBA
- Department: Sustainability Management
- R 6:10 p.m.-8 p.m.
- 3 points
ANALYTICS FOR BUSINESS OPERATIONS MANAGE
Operations Management (OM) is responsible for the efficient production and delivery of goods and services, serving as a cornerstone of successful organizations. This course emphasizes how analytical techniques, such as forecasting, queuing theory, and linear programming, provide critical tools for optimizing operational decision-making, improving efficiency, and addressing real-world challenges in operations management. In this course, you will gain essential skills to optimize processes, manage resources, and enhance productivity across various industries. The course will be delivered through a combination of interactive lectures, case studies, and hands-on coding exercises to ensure a balance between conceptual learning and practical application.
Through lectures, you will gain a solid foundation in OM principles and analytical techniques. Case studies will help illustrate real-world applications of OM in industries such as manufacturing, healthcare, retail, and logistics, allowing you to see how the concepts are applied in diverse contexts. This course will integrate the principles of OM with hands-on analytical techniques using Python, allowing you to model and solve real-world OM problems. You will learn to run simulations, perform optimizations, and analyze data to make data-driven decisions that enhance efficiency and overall performance.
OM practices are tailored to meet the specific needs of various sectors. In manufacturing, OM helps streamline production lines and minimize waste; in healthcare, it enhances patient flow and optimizes resource allocation; in retail, it improves inventory management and supply chain operations; and in logistics, it ensures timely deliveries while reducing transportation costs. This course will equip you with the skills to apply OM practices effectively in different industries.
Analytics for Business Operations Management is an elective that is intended for students who are interested in pursuing a career using analytics and operational insights to drive organizational success in a competitive global marketplace across various industries.
- Department: Applied Analytics
- F 12:10 p.m.-2 p.m.
- R 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
- 3 points
DERIVATIVES RISK MANAGEMENT
This course explores financial derivatives across different asset classes with in-depth analysis of several popular trades including block trades, program trades, vanilla options, digital options, and variance swaps. Their dynamics and risks are explored through Monte Carlo simulation using Excel and Python. The daily decisions and tasks of a frontline risk manager are recreated and students have the opportunity to see which trades they would approve or reject. Students will gain a working knowledge of financial derivatives and acquire technical skills to answer complex questions on the trading floor.
- Department: Enterprise Risk Management
- M 6:10 p.m.-8 p.m.
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Instructor:
Alberto Scalari
Info - 3 points
MANAGING STAKEHOLDER RISK IN THE ORGANIZ
In this course, students study major concepts of management and organization theory to understand human behavior in an organizational context, and then learn how to apply this to better manage interactions with key ERM stakeholders. Students will learn how to accomplish key ERM activities effectively while preserving and enhancing key internal relationships.
The course provides a deep dive into how enterprise risk functions operate within organizations, blending theoretical frameworks with practical, real-world applications. Topics include individual and organizational psychology, risk culture, organizational structure and governance, and the dynamics of managing risk in complex institutions. Through case studies and class discussions, students explore the behavioral and structural dimensions that shape ERM practices.
This elective is open only to students within the ERM program. This course (MSRO) is analogous to Managing Human Behavior in the Organization (MHBO), but customized for an ERM role. As a result, ERM students may not register for MHBO and those that have already taken MHBO may not register for MSRO.
- Department: Enterprise Risk Management
- R 6:10 p.m.-8 p.m.
- 3 points
FINANCIAL SEC ANALYSIS AND PORTFOLIO MGT
FIN SEC ANALYSIS AND PORT
Financial securities analysis and portfolio management is the study of analyzing information to evaluate financial securities and design investment strategies. Studying the subject can provide a foundation for students entering the fields of investment analysis or portfolio management. This course provides an intensive introduction to major topics in investments. Part I of the course lays the theoretical foundation by introducing the Portfolio Theory and Equilibrium Asset Pricing models. Part II covers the valuation models and analysis of major asset classes: equity, fixed-income, and derivatives. Topics include bond valuation and interest rate models, equity valuation and financial statement analysis, options valuation, other derivatives, and risk management. Part III of the course focuses on the practice of active portfolio management.
- Department: Applied Analytics
- W 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- None None-None
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Instructor:
Lei Yu
Info -
Instructor:
Alexandr Belyakov
Info - 3 points
TOOLS FOR RISK MANAGEMENT
Tools for Risk Management examines how risk technology platforms assess risks. These platforms gather, store, and analyze data; and transform that data to actionable information. This course explores how the platforms are implemented, customized, and evaluated. Topics include business requirements specification, data modeling, risk analytics and reporting, systems integration, regulatory issues, visualization, and change processes. Hands-on exercises using selected vendor tools will give students the opportunity to see what these tools can offer.
- Department: Enterprise Risk Management
- R 6:10 p.m.-8 p.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Barney Baldwin
Info -
Instructor:
Viktor Ziskin
Info - 3 points
MODEL RISK MANAGEMENT
Given the ever growing reliance on models, Model risk affects financial institutions at almost every level of their organization including pricing, risk, finance, and marketing. Model risk management (MRM) is now one of the primary focuses of operational risk management at modern financial institutions. In this class, the ERM skill sets of risk identification, risk quantification, and risk decision making are applied to the kinds of models seen in large, complex financial institutions. Through readings, lecture, assignments, and in-class discussions, students learn the principles and concepts that a robust MRM function uses to manage model risk.
- Department: Enterprise Risk Management
- R 8:10 p.m.-10 p.m.
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Instructor:
Peter L Russo
Info - 3 points
ERM MODELING
Equips students with the basics of risk measurement and simulation using a hands-on approach to ERM modeling. Using industry-standard simulation software, students build systems of risk drivers for finance and insurance companies. Topics include risk correlations, VaR and TVaR, capital modeling, capital allocation, and parameter, process, and model Risk. Students acquire both quantitative experience building models and qualitative appreciation for model weaknesses.
- Department: Enterprise Risk Management
- M 8:10 p.m.-10 p.m.
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Instructor:
Julian Horky
Info - 3 points